Channel estimation is a key ingredient of any communication system. In practice, channel estimation algorithms are designed to exploit the channel time and/or frequency correlations, or, in other words, the statistics of the channel. To exploit these correlations, typically, the channel's power delay profile (PDP) and the Doppler frequency shift needs to be estimated. Such estimates are known to be hard problems, and often only very crude assumptions are made regarding related unknowns while performing channel estimation. For instance, the PDP may be assumed to have a uniform distribution, while the Doppler frequency may only be characterized by two levels (e.g., either high or low). While such simple approximations are attractive from a complexity perspective, the result may be highly sub-optimal performance.
There is, therefore, a need in the art for improved estimation of channel time and frequency correlations.